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    <abstract>Pre-trained language models have shown superior performance in task-oriented dialogues. However, existing datasets are on limited scales, which cannot support large-scale pre-training. Fortunately, various data augmentation methods have been developed to augment large-scale task-oriented dialogue corpora. However, they heavily rely on annotated data in the target domain, which require a tremendous amount of data collection and human labeling work. In this paper, we build a unified dialogue user simulation model by pre-training on several publicly available datasets. The model can then be tuned on a target domain with few-shot data. The experiments on a target dataset across multiple domains show that our proposed model brings remarkable performance increases through data augmentation.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.277</identifier>
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%0 Conference Proceedings
%T A Unified Dialogue User Simulator for Few-shot Data Augmentation
%A Wan, Dazhen
%A Zhang, Zheng
%A Zhu, Qi
%A Liao, Lizi
%A Huang, Minlie
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F wan-etal-2022-unified
%X Pre-trained language models have shown superior performance in task-oriented dialogues. However, existing datasets are on limited scales, which cannot support large-scale pre-training. Fortunately, various data augmentation methods have been developed to augment large-scale task-oriented dialogue corpora. However, they heavily rely on annotated data in the target domain, which require a tremendous amount of data collection and human labeling work. In this paper, we build a unified dialogue user simulation model by pre-training on several publicly available datasets. The model can then be tuned on a target domain with few-shot data. The experiments on a target dataset across multiple domains show that our proposed model brings remarkable performance increases through data augmentation.
%R 10.18653/v1/2022.findings-emnlp.277
%U https://aclanthology.org/2022.findings-emnlp.277
%U https://doi.org/10.18653/v1/2022.findings-emnlp.277
%P 3788-3799

Markdown (Informal)

[A Unified Dialogue User Simulator for Few-shot Data Augmentation](https://aclanthology.org/2022.findings-emnlp.277) (Wan et al., Findings 2022)

• A Unified Dialogue User Simulator for Few-shot Data Augmentation (Wan et al., Findings 2022)

ACL

• Dazhen Wan, Zheng Zhang, Qi Zhu, Lizi Liao, and Minlie Huang. 2022. A Unified Dialogue User Simulator for Few-shot Data Augmentation. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3788–3799, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.